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import argparse
import torch
import os
import json
from tqdm import tqdm
from minigemini.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from minigemini.conversation import conv_templates, SeparatorStyle
from minigemini.model.builder import load_pretrained_model
from minigemini.utils import disable_torch_init
from minigemini.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
from PIL import Image
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
def create_one_query(problem, shot_num, shot_type, use_caption):
### [1] Demo prompt
demo_prompt = ""
### [2] Test query
# problem info
question = problem['question']
unit = problem['unit']
choices = problem['choices']
# caption = problem['caption']
precision = problem['precision']
question_type = problem['question_type']
answer_type = problem['answer_type']
# hint
if shot_type == 'solution':
if question_type == "multi_choice":
assert answer_type == "text"
hint_text = f"Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end."
else:
assert answer_type in ["integer", "float", "list"]
if answer_type == "integer":
hint_text = f"Hint: Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end."
elif answer_type == "float" and precision == 1:
hint_text = f"Hint: Please answer the question requiring a floating-point number with one decimal place and provide the final value, e.g., 1.2, 1.3, 1.4, at the end."
elif answer_type == "float" and precision == 2:
hint_text = f"Hint: Please answer the question requiring a floating-point number with two decimal places and provide the final value, e.g., 1.23, 1.34, 1.45, at the end."
elif answer_type == "list":
hint_text = f"Hint: Please answer the question requiring a Python list as an answer and provide the final list, e.g., [1, 2, 3], [1.2, 1.3, 1.4], at the end."
else:
assert shot_type == 'code'
hint_text = "Hint: Please generate a python code to solve the problem"
# question
question_text = f"Question: {question}"
if unit:
question_text += f" (Unit: {unit})"
# choices
if choices:
# choices: (A) 1.2 (B) 1.3 (C) 1.4 (D) 1.5
texts = ["Choices:"]
for i, choice in enumerate(choices):
texts.append(f"({chr(ord('A')+i)}) {choice}")
choices_text = "\n".join(texts)
else:
choices_text = ""
# prompt
if shot_type == 'solution':
prompt = "Solution: "
else:
assert shot_type == 'code'
prompt = "Python code: "
elements = [hint_text, question_text, choices_text]
test_query = "\n".join([e for e in elements if e != ""])
### [3] Final query
query = demo_prompt + "\n\n" + test_query
query = query.strip()
return query
def eval_model(args):
# Model
disable_torch_init()
model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name,
load_8bit=args.load_8bit)
questions = json.load(open(os.path.expanduser(args.question_file), "r"))
questions = [dict(pid=pid, info=qs) for pid, qs in questions.items()]
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
if os.path.exists(answers_file):
file = open(answers_file, "r")
pred_contents = [json.loads(line) for line in file]
done_pid = [sample['pid'] for sample in pred_contents]
else:
done_pid = []
ans_file = open(answers_file, "a")
for i, line in enumerate(tqdm(questions)):
idx = line['pid']
info = line['info']
if idx in done_pid:
continue
qs = create_one_query(
problem = info,
shot_num = 0,
shot_type = 'solution',
use_caption = False,
)
query = qs
if 'image' in info:
image_file = info["image"]
image = Image.open(os.path.join(args.image_folder, image_file))
if hasattr(model.config, 'image_size_aux'):
if not hasattr(image_processor, 'image_size_raw'):
image_processor.image_size_raw = image_processor.crop_size.copy()
image_processor.crop_size['height'] = model.config.image_size_aux
image_processor.crop_size['width'] = model.config.image_size_aux
image_processor.size['shortest_edge'] = model.config.image_size_aux
image_tensor = process_images([image], image_processor, model.config)[0]
image_grid = getattr(model.config, 'image_grid', 1)
if hasattr(model.config, 'image_size_aux'):
raw_shape = [image_processor.image_size_raw['height'] * image_grid,
image_processor.image_size_raw['width'] * image_grid]
image_tensor_aux = image_tensor
image_tensor = torch.nn.functional.interpolate(image_tensor[None],
size=raw_shape,
mode='bilinear',
align_corners=False)[0]
else:
image_tensor_aux = []
if image_grid >= 2:
raw_image = image_tensor.reshape(3,
image_grid,
image_processor.image_size_raw['height'],
image_grid,
image_processor.image_size_raw['width'])
raw_image = raw_image.permute(1, 3, 0, 2, 4)
raw_image = raw_image.reshape(-1, 3,
image_processor.image_size_raw['height'],
image_processor.image_size_raw['width'])
if getattr(model.config, 'image_global', False):
global_image = image_tensor
if len(global_image.shape) == 3:
global_image = global_image[None]
global_image = torch.nn.functional.interpolate(global_image,
size=[image_processor.image_size_raw['height'],
image_processor.image_size_raw['width']],
mode='bilinear',
align_corners=False)
# [image_crops, image_global]
raw_image = torch.cat([raw_image, global_image], dim=0)
image_tensor = raw_image.contiguous()
images = image_tensor[None].to(dtype=model.dtype, device='cuda', non_blocking=True)
images_aux = image_tensor_aux[None].to(dtype=model.dtype, device='cuda', non_blocking=True) if len(image_tensor_aux)>0 else None
if getattr(model.config, 'mm_use_im_start_end', False):
qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
else:
qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
else:
images = None
images_aux = None
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images,
images_aux=images_aux,
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=1024,
bos_token_id=tokenizer.bos_token_id, # Begin of sequence token
eos_token_id=tokenizer.eos_token_id, # End of sequence token
pad_token_id=tokenizer.pad_token_id, # Pad token
use_cache=True,
)
outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
info['query'] = query
info['response'] = outputs
ans_file.write(json.dumps(info) + "\n")
ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
parser.add_argument("--model-base", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.json")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--conv-mode", type=str, default="llava_v0")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
parser.add_argument("--temperature", type=float, default=0.2)
parser.add_argument("--answer-prompter", action="store_true")
parser.add_argument('--load_8bit', type=bool, default=False)
parser.add_argument("--single-pred-prompt", action="store_true")
args = parser.parse_args()
eval_model(args) |